abstract = "This work proposes an automatic methodology for
modelling complex systems. Our methodology is based on
the combination of Grammatical Evolution and classical
regression to obtain an optimal set of features that
take part of a linear and convex model. This technique
provides both Feature Engineering and Symbolic
Regression in order to infer accurate models with no
effort or designer's expertise requirements. As
advanced Cloud services are becoming mainstream, the
contribution of data centers in the overall power
consumption of modern cities is growing dramatically.
These facilities consume from 10 to 100 times more
power per square foot than typical office buildings.
Modeling the power consumption for these
infrastructures is crucial to anticipate the effects of
aggressive optimisation policies, but accurate and fast
power modelling is a complex challenge for high-end
servers not yet satisfied by analytical approaches. For
this case study, our methodology minimises error in
power prediction. This work has been tested using real
Cloud applications resulting on an average error in
power estimation of 3.98percent. Our work improves the
possibilities of deriving Cloud energy efficient
policies in Cloud data centers being applicable to
other computing environments with similar
characteristics.",